





'''simple docstring'''






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowerCAmelCase									=   logging.getLogger(__name__)
@dataclass
class 		lowerCamelCase	:
	snake_case_   = field(
	    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Pretrained config name or path if not the same as model_name"}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   , )
	snake_case_   = field(default=_A   , metadata={"help": "Whether tp freeze the encoder."}							)
	snake_case_   = field(default=_A   , metadata={"help": "Whether  to freeze the embeddings."}							)






@dataclass
class 		lowerCamelCase	:
	snake_case_   = field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}							)
	snake_case_   = field(
	    default="summarization"   , metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}   , )
	snake_case_   = field(
	    default=1_024   , metadata={
	        "help": (
	            "The maximum total input sequence length after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }   , )
	snake_case_   = field(
	    default=128   , metadata={
	        "help": (
	            "The maximum total sequence length for target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }   , )
	snake_case_   = field(
	    default=142   , metadata={
	        "help": (
	            "The maximum total sequence length for validation target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded. "
	            "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
	            "during ``evaluate`` and ``predict``."
	        )
	    }   , )
	snake_case_   = field(
	    default=142   , metadata={
	        "help": (
	            "The maximum total sequence length for test target text after tokenization. Sequences longer "
	            "than this will be truncated, sequences shorter will be padded."
	        )
	    }   , )
	snake_case_   = field(default=-1   , metadata={"help": "# training examples. -1 means use all."}							)
	snake_case_   = field(default=-1   , metadata={"help": "# validation examples. -1 means use all."}							)
	snake_case_   = field(default=-1   , metadata={"help": "# test examples. -1 means use all."}							)
	snake_case_   = field(default=_A   , metadata={"help": "Source language id for translation."}							)
	snake_case_   = field(default=_A   , metadata={"help": "Target language id for translation."}							)
	snake_case_   = field(default=_A   , metadata={"help": "# num_beams to use for evaluation."}							)
	snake_case_   = field(
	    default=_A   , metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}   , )






def   __A    (				a_    :	Tuple   ,a_    :	str   ,a_    :	Union[str, Any]						):
		logger.info(f'''***** {split} metrics *****'''						)
		for key in sorted(metrics.keys()						):
				logger.info(f'''  {key} = {metrics[key]}'''						)
		save_json(a_   ,os.path.join(a_   ,f'''{split}_results.json'''						)						)






def   __A    (				):
		# See all possible arguments in src/transformers/training_args.py
		# or by passing the --help flag to this script.
		# We now keep distinct sets of args, for a cleaner separation of concerns.

		lowerCAmelCase  :     Any       						= HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)						)

		if len(sys.argv						) == 2 and sys.argv[1].endswith(".json"						):
				# If we pass only one argument to the script and it's the path to a json file,
				# let's parse it to get our arguments.
				lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]						)						)
		else:
				lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     str       						= parser.parse_args_into_dataclasses()

		check_output_dir(a_						)

		# Setup logging
		logging.basicConfig(
		    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,datefmt="%m/%d/%Y %H:%M:%S"   ,level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN   ,)
		logger.warning(
		    "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"   ,training_args.local_rank   ,training_args.device   ,training_args.n_gpu   ,bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED						)   ,training_args.fpaa   ,)
		transformers.utils.logging.enable_default_handler()
		transformers.utils.logging.enable_explicit_format()
		# Set the verbosity to info of the Transformers logger (on main process only):
		if is_main_process(training_args.local_rank						):
				transformers.utils.logging.set_verbosity_info()
		logger.info("Training/evaluation parameters %s"   ,a_						)

		# Set seed
		set_seed(training_args.seed						)

		# Load pretrained model and tokenizer
		#
		# Distributed training:
		# The .from_pretrained methods guarantee that only one local process can concurrently
		# download model & vocab.

		lowerCAmelCase  :     List[Any]       						= AutoConfig.from_pretrained(
		    model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,cache_dir=model_args.cache_dir   ,)

		lowerCAmelCase  :     int       						= ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
		for p in extra_model_params:
				if getattr(a_   ,a_   ,a_						):
						assert hasattr(a_   ,a_						), f'''({config.__class__.__name__}) doesn\'t have a `{p}` attribute'''
						setattr(a_   ,a_   ,getattr(a_   ,a_						)						)

		lowerCAmelCase  :     Optional[int]       						= AutoTokenizer.from_pretrained(
		    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,cache_dir=model_args.cache_dir   ,)
		lowerCAmelCase  :     Dict       						= AutoModelForSeqaSeqLM.from_pretrained(
		    model_args.model_name_or_path   ,from_tf=".ckpt" in model_args.model_name_or_path   ,config=a_   ,cache_dir=model_args.cache_dir   ,)

		# use task specific params
		use_task_specific_params(a_   ,data_args.task						)

		# set num_beams for evaluation
		if data_args.eval_beams is None:
				lowerCAmelCase  :     str       						= model.config.num_beams

		# set decoder_start_token_id for MBart
		if model.config.decoder_start_token_id is None and isinstance(a_   ,(MBartTokenizer, MBartTokenizerFast)						):
				assert (
				    data_args.tgt_lang is not None and data_args.src_lang is not None
				), "mBart requires --tgt_lang and --src_lang"
				if isinstance(a_   ,a_						):
						lowerCAmelCase  :     List[str]       						= tokenizer.lang_code_to_id[data_args.tgt_lang]
				else:
						lowerCAmelCase  :     str       						= tokenizer.convert_tokens_to_ids(data_args.tgt_lang						)

		if model_args.freeze_embeds:
				freeze_embeds(a_						)
		if model_args.freeze_encoder:
				freeze_params(model.get_encoder()						)
				assert_all_frozen(model.get_encoder()						)

		lowerCAmelCase  :     Tuple       						= SeqaSeqDataset

		# Get datasets
		lowerCAmelCase  :     int       						= (
		    dataset_class(
		        a_   ,type_path="train"   ,data_dir=data_args.data_dir   ,n_obs=data_args.n_train   ,max_target_length=data_args.max_target_length   ,max_source_length=data_args.max_source_length   ,prefix=model.config.prefix or ""   ,)
		    if training_args.do_train
		    else None
		)
		lowerCAmelCase  :     List[Any]       						= (
		    dataset_class(
		        a_   ,type_path="val"   ,data_dir=data_args.data_dir   ,n_obs=data_args.n_val   ,max_target_length=data_args.val_max_target_length   ,max_source_length=data_args.max_source_length   ,prefix=model.config.prefix or ""   ,)
		    if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
		    else None
		)
		lowerCAmelCase  :     Tuple       						= (
		    dataset_class(
		        a_   ,type_path="test"   ,data_dir=data_args.data_dir   ,n_obs=data_args.n_test   ,max_target_length=data_args.test_max_target_length   ,max_source_length=data_args.max_source_length   ,prefix=model.config.prefix or ""   ,)
		    if training_args.do_predict
		    else None
		)

		# Initialize our Trainer
		lowerCAmelCase  :     Any       						= (
		    build_compute_metrics_fn(data_args.task   ,a_						) if training_args.predict_with_generate else None
		)
		lowerCAmelCase  :     Tuple       						= SeqaSeqTrainer(
		    model=a_   ,args=a_   ,data_args=a_   ,train_dataset=a_   ,eval_dataset=a_   ,data_collator=SeqaSeqDataCollator(
		        a_   ,a_   ,model.config.decoder_start_token_id   ,training_args.tpu_num_cores						)   ,compute_metrics=a_   ,tokenizer=a_   ,)

		lowerCAmelCase  :     int       						= {}
		# Training
		if training_args.do_train:
				logger.info("*** Train ***"						)

				lowerCAmelCase  :     Union[str, Any]       						= trainer.train(
				    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path						) else None						)
				lowerCAmelCase  :     List[Any]       						= train_result.metrics
				lowerCAmelCase  :     Optional[Any]       						= data_args.n_train

				trainer.save_model()  # this also saves the tokenizer

				if trainer.is_world_process_zero():
						handle_metrics("train"   ,a_   ,training_args.output_dir						)
						all_metrics.update(a_						)

						# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
						trainer.state.save_to_json(os.path.join(training_args.output_dir   ,"trainer_state.json"						)						)

						# For convenience, we also re-save the tokenizer to the same directory,
						# so that you can share your model easily on huggingface.co/models =)
						tokenizer.save_pretrained(training_args.output_dir						)

    # Evaluation
		if training_args.do_eval:
				logger.info("*** Evaluate ***"						)

				lowerCAmelCase  :     List[str]       						= trainer.evaluate(metric_key_prefix="val"						)
				lowerCAmelCase  :     List[Any]       						= data_args.n_val
				lowerCAmelCase  :     Tuple       						= round(metrics["val_loss"]   ,4						)

				if trainer.is_world_process_zero():
						handle_metrics("val"   ,a_   ,training_args.output_dir						)
						all_metrics.update(a_						)

		if training_args.do_predict:
				logger.info("*** Predict ***"						)

				lowerCAmelCase  :     int       						= trainer.predict(test_dataset=a_   ,metric_key_prefix="test"						)
				lowerCAmelCase  :     Any       						= test_output.metrics
				lowerCAmelCase  :     str       						= data_args.n_test

				if trainer.is_world_process_zero():
						lowerCAmelCase  :     Optional[int]       						= round(metrics["test_loss"]   ,4						)
						handle_metrics("test"   ,a_   ,training_args.output_dir						)
						all_metrics.update(a_						)

						if training_args.predict_with_generate:
								lowerCAmelCase  :     Union[str, Any]       						= tokenizer.batch_decode(
								    test_output.predictions   ,skip_special_tokens=a_   ,clean_up_tokenization_spaces=a_						)
								lowerCAmelCase  :     List[Any]       						= lmap(str.strip   ,a_						)
								write_txt_file(a_   ,os.path.join(training_args.output_dir   ,"test_generations.txt"						)						)

		if trainer.is_world_process_zero():
				save_json(a_   ,os.path.join(training_args.output_dir   ,"all_results.json"						)						)

		return all_metrics






def   __A    (				a_    :	Union[str, Any]						):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
					main()










'''simple docstring'''






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for data in source_data:
				for i, el in enumerate(a_						):
						if len(a_						) < i + 1:
								data_lists.append([]						)
						data_lists[i].append(float(a_						)						)
		return data_lists






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     list[list[float]]       						= []
		for dlist, weight in zip(a_   ,a_						):
				lowerCAmelCase  :     Optional[Any]       						= min(a_						)
				lowerCAmelCase  :     List[str]       						= max(a_						)

				lowerCAmelCase  :     list[float]       						= []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))						)
								except ZeroDivisionError:
										score.append(1						)

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)						)
								except ZeroDivisionError:
										score.append(0						)

        # weight not 0 or 1
				else:
						lowerCAmelCase  :     List[Any]       						= f'''Invalid weight of {weight:f} provided'''
						raise ValueError(a_						)

				score_lists.append(a_						)

		return score_lists






def   __A    (				a_    :	list[list[float]]						):
		lowerCAmelCase  :     list[float]       						= [0 for i in range(len(score_lists[0]						)						)]

		for slist in score_lists:
				for j, ele in enumerate(a_						):
						lowerCAmelCase  :     Optional[Any]       						= final_scores[j] + ele

		return final_scores






def   __A    (				a_    :	list[list[float]]   ,a_    :	list[int]						):
		lowerCAmelCase  :     Union[str, Any]       						= get_data(a_						)
		lowerCAmelCase  :     List[Any]       						= calculate_each_score(a_   ,a_						)
		lowerCAmelCase  :     Optional[Any]       						= generate_final_scores(a_						)

		# append scores to source data
		for i, ele in enumerate(a_						):
				source_data[i].append(a_						)

		return source_data



